How is Data Sharing Changing the Healthcare Industry and the Professional Roles in it? Literature Review

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How is Data Sharing Changing the Healthcare Industry and the Professional

Roles in it? Literature Review

Pihla Orkoneva S3526860

Supervisor: Prof. Dr. David Langley 2nd Supervisor: Dr. Ileana Maris-de Bresser

June 2020

Word count: 11064 (excluding references and appendices) Faculty of Economics and Business

MSc BA Change Management Master Thesis

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Abstract

Purpose: The purpose of this study is to map the current landscape of how sharing patient-generated data could change the healthcare industry and the roles of professionals in it.

Methods: This study is conducted as a narrative literature review, including 21 articles concerning the changing field of healthcare and the professionals in it.

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Introduction

Vast amounts of health-related data are continuously being generated (Sultan, 2015) as a result of the fast development of technology, such as Internet of Things (IoT), wearable technologies, and smartphones. However, the healthcare industry is not actively taking advantage of this ‘big data’ created (Zheng et al., 2019). People are using wearables, for example smartwatches and activity bracelets, in increasing amounts, and the amount of healthy lifestyle -related applications for smartphones skyrocketed in the past few years. In 2015, over 58% of smartphone owners had downloaded a health-related application to their mobile phones (Bhuyan et al., 2017). People are tracking their normal, everyday activities, such as eating and sleeping habits, and heart rate, but how could it be made sure that this data would be used to make the healthcare better and thus our lives better? All this data could have an immense impact on the healthcare industry and the role of professionals in it. However, sharing this data has its difficulties concerning the privacy, which technologies to use to keep the data safe, analyzing of the data, and leveraging it effectively, and it could have multiple unknown impacts on the whole industry.

The research around the topic of data sharing in the healthcare industry has been revolving around the privacy and security issues and trying to find solutions to how this sharing of data could be handled securely (Hao et al., 2019; Shen et al., 2019). Great amount of the research shows the importance of self-sovereignty in data sharing; people want to be in control of what they share, how they share it, and to whom they share it to (Kim et al., 2015; Spencer et al., 2016; Weitzman et al., 2010). It has already been concluded that data innovation can dramatically change the healthcare industry (Tang et al., 2018), and that there is a possibility of professional roles shifting into a more preventive role (Chung et al., 2016). This change would lead the hospitals and practitioners to go through digital transformation and develop intelligent healthcare, which is the new stage of IT application in the healthcare field that is supported by the IoT technology (Zheng & Rodriguez-Monroy, 2015).

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how this data is being generated, so we can understand how it can be shared. There are also contingencies in the sharing process, which need to be understood so it can be leveraged. All this need to be comprehended in order to understand where this change is taking the industry and thus the roles of professionals.

In this study, I conducted a narrative literature review to map the current landscape on the knowledge of how data sharing can change the industry and what the effect is on professional roles. Data in this context means the data generated by patients themselves, by using for example smartwatches. Hospitals exchanging data among themselves is not considered, since this has been already widely researched (Gordon & Catalini, 2018). I will look into the different ‘how’s’ and ‘what’s’ to create an understanding of where the research currently is, and where there is still a need for further research. Increasing the awareness of data sharing and its possibilities in the healthcare industry is an important step towards leveraging the generated data, and thus improving the healthcare sector. The impact of healthcare taking advantage of this data could be huge of the whole sector. The research question I try to answer is twofold: 1) How data sharing is changing the healthcare industry? and 2) What is the effect of this change on professional roles?

This study contributes to the existing literature by being the first study to gather together all the current knowledge regarding this topic. The study combines the knowledge and introduces concurring themes in the literature. It also contributes to the understanding of the direction the healthcare industry might be going because of this possibility of data sharing, as well as the possibility of changing professional roles. The researchers before have been looking into specific technological possibilities or security issues. I will add to this literature by trying to generalize the findings of these studies into industry-wide analysis, which has not been done before. At the end of the study, I will give possible future research areas.

The managerial implications of this study will be especially for the members of the industry, to better understand what is happening and give managers in healthcare an idea of what to prepare for. Executives tend to spend too little time on scanning the external environment, the changes in it, and its possible implications for their organizations (Cawsey et al., 2016). As one goal of a review is to save time from other professionals when searching for information (Green et al., 2006), this study might help the executives to realize the importance of the effects this digital transformation can have on the industry, and the need to adapt to it easier.

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Methodology

In this section, I go over the reasons for conducting a literature review and why I chose the narrative type. Then, I elaborate on the methods used to conduct the review, including the definition of search terms, listing the search queries, and explanation of how I formulated the findings.

Why Literature Review?

Conducting literature reviews is essential for gathering the current information on a certain topic and for identifying new research directions. By doing this, we can identify patterns and gaps within the literature, and predict the direction of future research (Jones & Gatrell, 2014; Webster & Watson, 2002). Literature reviews have other benefits as well, for example following:

“As compared with empirical reports, literature reviews can tackle broader and more abstract questions, can engage in more post hoc theorizing without the danger of capitalizing on chance, can make a stronger case for a null-hypothesis conclusion, and can appreciate and use methodological diversity better.” (Baumeister & Leary, 1997, p.311)

Baumeister and Leary (1997) propose five main goals of conducting a literature review, namely theory development, theory evaluation, state of knowledge on a particular topic, problem identification, and/or to provide a historical account. The first two goals are the most ambitious and are about building or testing of theory and are not the goal of this review. This study’s goals are to state the knowledge on a topic of how data sharing is changing the health care industry and its effect on professionals, as well as to identify problems in the research area. These problems can be for example contradictions or controversies in the area of investigation. Providing a historical account is not a goal of this study.

Narrative Review.

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Methods

The review was conducted following the article “Writing narrative literature reviews for peer-reviewed journals: Secrets of the trade” by Green et al. (2006). The authors propose that the author of a review should first get familiar with the existing literature and define the source of information. The main search engine I used was Web of Science to ensure the quality of the articles and that all the articles are peer-reviewed. I got familiar with the existing literature by experimenting with different search words, such as “intelligent healthcare”, “data sharing AND healthcare”, to try to narrow down the topic. I found that the managerial literature on any of these topics is extremely limited.

The next step in Green et al. (2006) is to define search terms and delimiting. First, I glanced through multiple articles that seemed suitable, and mapped out the area of interest. This way I found the following search words to be relevant for my topic: health care (or healthcare), data sharing, intelligent healthcare, big data, digitalization (or digitalisation), digital transformation, business intelligence and analytics (or BI&A or business intelligence), wearables, and mobile health (or mHealth). I decided to include all of these, because of the limited number of studies available that specifically touch the topic of patient-generated data, and they all provide relevant information for the topic. I rejected some terms being out of scope, for example e-health and electronic health records (EHR’s), since they do not provide any relevant knowledge for the data sharing from the patient-side. They rather concentrate on one-way of sharing data, where the professionals provide patients with information. They do play a role in the new ways as well, but with the current search terms those studies that suit this study were already included.

Definition of search terms.

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Search term Definition

Healthcare

Health care services and facilities, meaning hospitals, nursing and residential care, ambulatory healthcare services, and medical practitioners (Wilston, 2019).

Data sharing

Private individuals using for example wearables and thus generating data, and further allowing it to be collected and used by the healthcare provider networks (Tang et al. 2018). Without the data generated by patients, patient information is incomplete and can cause for example higher mortality (Kohli & Tan, 2016). In order to get business value from big data, it needs to be shared first (Wang & Hajli, 2017).

Intelligent healthcare

Healthcare industry is assumed to change from digital healthcare into intelligent healthcare - new stage of IT application in the health care field that is supported by the IoT technology (Zheng & Rodriguez-Monroy, 2015). It is called intelligent since data is used to guide decisions and to train smart healthcare systems in supporting and alerting the healthcare professionals (Xie et al., 2020).

Big data

Data sets that are extremely large and complex that they “require advanced and unique data storage, management, analysis, and visualization techniques.” (Chen et al., 2012, p. 1166) In Big Data I concentrate on data generated by patients themselves, and not the data generated through traditional healthcare systems.

Digitalization Use of IT or digital technologies to alter existing business processes (Verhoef et al., 2019)

Digital transformation

“Change in how a firm employs digital technologies, to develop a new digital business model that helps to create and appropriate more value for the firm.” (Verhoef et al., 2019, p. 1)

Business intelligence and analytics

“Techniques, technologies, systems, practices, methodologies, and applications that analyze critical business data.” (Chen et al., 2012, p. 1166)

Wearables Computing devices, such as smart watches, that can be worn and can perform variety of functions (Sultan, 2015).

Mobile health

“Medical and public health practice supported by mobile devices such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), tablets and other handheld devices” (World Health Organization, 2011, p. 6)

Table 1 – Definitions of search terms

Searching and mapping.

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Search Query Number of Results

“healthcare OR “health care”” AND “data sharing” 8 “intelligent healthcare” OR “intelligent health care” 2 “healthcare OR “health care”” AND “big data” 100 “healthcare OR “health care”” AND “digitalization OR

digitalisation” 14

“healthcare OR “health care”” AND “digital

transformation” 10

“healthcare OR “health care”” AND “” business intelligence and analytics” OR BI&A OR “business

intelligence””

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“healthcare OR “health care”” AND “wearables” 5 “healthcare OR “health care”” AND “” mobile health”

OR mHealth” 49

Table 2 – Search Queries

In total, I got 203 results, and noticed that some of the results are overlapping. I combined all the queries together with OR in between, the number of results was 186. The oldest article was from 1998 and the latest from 2020. The trend is not surprising to see, in 1998, there was one article found, the next article was from 2007, and from there the number of results grows slowly. In Appendix 1, the development of the number of articles over the years can be seen in total. Here we can see that this topic is rather new.

I read all the titles and abstracts, and from these, I excluded some articles either being off topic by not being related to the healthcare industry or data sharing. In some cases, healthcare was mentioned as an example, and some cases of big data were only related to traditional health care systems, which was already delimited. I found sixty-four articles that seemed relevant to the study based on their titles and abstracts. I read the articles and selected eighteen relevant articles for the review. From the selected articles I conducted further snowballing and found three other relevant articles from their reference lists, so the overall number of articles in this review is twenty-one. All the articles selected were written in English.

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The last two review mHealth from the perspective of the impact of it on different levels in a very broad manner (Varshney, 2014a) and the influence it can have on patient and healthcare provider relationship (Qudah & Luetsch, 2019).

The exclusion of the articles was based on some information being already outdated. The digital transformation in healthcare sector had not yet been started properly in these articles. The discussion in and before 2010 was revolving around how to digitalize systems and use computers overall (Zhang et al., 2017). Some articles discussed data sharing, but from the perspective of doctors sharing data to patients in mHealth. Rest of the articles were completely out of scope.

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Findings

The findings section is constructed followingly: first, I give an introduction to data in healthcare, how the data is generated and shared, and how it can be leveraged. Second, I go over the possible benefits and challenges the data sharing could bring, and third, what is the direction of the change on the healthcare industry level, what strategies could be used to get to that direction, and what would be the needed changes. Third, I go over the impact of this on the healthcare professionals and their roles.

Data in Healthcare

Current challenges in healthcare include lack of efficiency in identifying diseases in early stages (Huang et al., 2017) and incomplete patient data which leads to a significant number of medical errors (Kohli & Tan, 2016). These medical errors lead to false diagnosis and treatments, which can have serious consequences (Pramanik et al., 2017). Each year, worldwide, patients stay approximately 2.4 million extra days in the hospital because of these false diagnoses made by the healthcare professionals, as well as cause 32,000 deaths, and $9 billion (~8,2 billion euros) additional costs.

New technologies and the data generated by patients could be used for enhancing the quality and breadth of health services. Currently, in the healthcare sector, there has been an ongoing discussion about the interoperability of the IT systems, but - “… Even if the different systems were highly interoperable, there would still be missing data – personal device monitor data, lifestyle behavior, social determinants of health – that is generated by patients” (Gordon & Catalini, 2018. p.225). Medical care is currently seen as only one of the determinants for health (Hiller, 2016). Other determinants are, on top of the abovementioned, genetics, and environmental and physical influences. There are as well behavioral determinants that can undermine health, including, for example, smoking and drug abuse. As mentioned before, most of the smartphone users have downloaded a health-related application. With the opportunity to collect all this data related to personal health, lifestyle and environment, these mobile applications have the potential to improve the quality of patient care, while simultaneously having the potential of reducing costs (Bhuyan et al., 2017).

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Smart cities (Pramanik et al., 2017; Huang et al., 2017). Smart homes have sensors to monitor all the vital functions of a person and matters affecting the health of a person, such as temperature, at all times. These sensors could also sense if something is medically wrong with the patient and trigger a message to be sent automatically to necessary parties. This data could help doctors to make better decisions on their patients, which would decrease the number of errors made. However, it is not enough to only collect this data; it needs to be analyzed using big data analytics (Wang et al., 2018).

Collecting, processing, analyzing, and visualizing data.

Healthcare technologies have been progressing tremendously over the past decade, but at the same time, the health-related data is growing bigger and bigger (Zhang et al., 2017). The data being handled is the so-called big data. Big data is originally characterized by “3V’s”, volume, variety, and velocity (Wang & Hajli, 2017). Volume refers to the amount of available data, velocity to the massive arrival rate of this data, and variety to the existence of data from multiple different sources in different formats (Palanisamy & Thirunavukarasu, 2019). However, in the healthcare sector, these characteristics are redefined into silo, security, and variety. Silo refers to the legacy database with public healthcare information, security points out the extra care needed in handling health-related data, and variety remains the same as traditionally defined.

To be able to analyze and take advantage of the big data, it needs to be first collected, then processed and transformed into understandable information, and finally visualized (Wang & Hajli, 2017). However, there are multiple difficulties in achieving this. As there is a tremendous number of users, and a real-time data from mobiles phones, sensors or wearables are collected, even collecting all this data on servers becomes expensive and difficult (Jian et al., 2016; Zhang et al., 2017). The technology of Business intelligence & analytics (BI&A) can support in these challenges. BI&A technology is switching from BI&A 2.0, which is characterized by web analytics and user-generated content from social and crowdsourcing systems, into BI&A 3.0. BI&A 3.0 is the mobile analytics and context-aware techniques for collecting, processing, analyzing, and visualizing large-scale mobile and sensor data (Chen et al., 2012). For healthcare, Intelligent Data Forwarder has been suggested for collecting data from users (Jian et al., 2016; Varshney, 2014b). This forwarder has the context-awareness built-in, which means, that it can programmed to detect particular behavior, and label it as essential. It can be determined, for example, that the data is only forwarded in cases where the individual is exercising, or there is an anomaly detected in the patients vital functions. It will only transmit the labelled information to the big data server to be analyzed further.

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2017). Another model, without an intelligent forwarder, proposes an architecture for big data processing. The model includes a data collection layer, which includes adapters that can process data from different structures in a secure way. However, this model only cleans data, removes redundancies, and does compression, but forwards all the data to the file storage without filtering it further. Without filtering, as the Intelligent Information Forwarder does, the amount of data is huge. This alone can already bring challenges.

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Statistics-based

applications Descriptive analysis

Explains what happened in the past – clinical summaries on an individual’s health

Monitoring-based applications

Illustrates vital signs of an individual in real time reporting – descriptive analytics can be used afterwards to see what happened

Knowledge-based applications

To discover data correlations and dependencies

Prediction-based applications

Predictive analysis

Predicting the future and explaining what might happen – taking into account different attributes of a patient and identifying causalities and

patterns

Prescriptive analysis Suggesting optimal solutions to predictive analysis results based on different attributes

Table 3 – Relationship between application and analysis type

Lastly, after the data has been analyzed, it needs to be visualized. Visualization is vital for healthcare professionals. The information needs to be easy to understand, so the professionals can base decisions on it (Wang & Hajli, 2017; Varshney, 2014b). There are three functionalities in visualization of data – general clinical summaries, data visualization, and real-time reporting (Wang & Hajli, 2017). The general clinical summaries are such as created from the statistics-based applications and can support evidence-based medicine. Data visualization is then a more visual way of deducting meanings from data, such as interactive dashboards. These support healthcare professionals everyday decisions to be faster and to be based in data. Real-time reporting includes alerts and proactive notifications, and can be also made available in dashboards.

Even with the context-awareness, there can still be too much data for a human to process. As the volume of data grows, healthcare professionals are concerned with missing vital clues in treating patients (Kohli & Tan, 2016). The context-awareness and visualization of the data can help the professionals in healthcare to decrease the cognitive overload they might experience with the increasing amount of data (Varshney, 2014b). If the professionals experience this overload, it will affect the quality of their decision-making, and thus lead to more errors and decrease the quality of healthcare. When there is only selected information available and the display is improved, for example, by color-coding, this data can be useful and improve the quality of decisions.

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possible direction of the changes, and what is needed for this change to happen in the healthcare industry.

Benefits and Challenges in Data Sharing

Benefits.

All the literature on the topic agrees on the fact that there would be a tremendous number of benefits from utilizing this patient-generated data. There are few studies that concentrate mostly just on these benefits (Salman et al., 2017; Wang & Hajli, 2017; Wang et al., 2018). The discussed benefits here are improved decision-making (Varshney, 2014a) which leads to improved quality of healthcare (Wang et al., 2018), fairness in selecting the patients to receive treatment (Salman et al., 2017), and benefits in IT infrastructure and operations on organizational level (Wang & Byrd, 2017).

The most discussed benefit is the improved decision-making, which is based on data (Varshney, 2014a; Wang et al., 2018; Pramanik et al., 2017). First, the quality of decisions made by the healthcare professional improves (Varshney, 2014a), which would have a further impact as decreased number of medical errors. Healthcare professionals make decisions traditionally under pressure, and they must consider multiple factors to pick - what they think based on available information - the best option. Especially in emergencies, the speed of decision-making is essential, and the professionals might make mistakes. Usually, they also do not have all the information needed, so the patient data is incomplete. Thus, when they have more complete patient data, information-related errors are reduced. As discussed before, the false diagnoses made by healthcare professionals has a huge effect on the extra days of stay in hospitals, avoidable deaths, and costs. It is even argued that big data generates value only when applied to make better and faster decisions (Pramanik et al., 2017). In other words, the benefit would be in moving to evidence-based decision-making, and not accepting any decisions that cannot be proven without data (Ratia et al., 2019). The use of the data would thus improve the quality of healthcare services through more accurate analysis (Wang et al., 2018). These healthcare services include, for example, improved overall healthcare and better medical treatment (Hiller, 2016).

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of medical errors, and especially deaths, since the patients who really need the treatment first would receive it.

Other potential benefits of leveraging big data are in IT infrastructure and operations (Wang & Byrd, 2017; Wang & Hajli, 2017; Wang et al., 2018). In IT infrastructure the most notable benefits can be found from reducing system redundancy, avoiding unnecessary IT costs, and transferring data quickly among healthcare IT systems. The operational benefits include already mentioned improved quality and accuracy in decision-making, and shortening the time of the diagnostic test. Both of the IT infrastructural and operational benefits would bring cost reductions to the organization (Wang et al., 2018). However, the evidence for managerial, strategic, and organizational benefits are not overwhelming. There is some proof found for each, but because of the early stage of development of big data analytics in healthcare, the benefits are mostly found in IT infrastructure and operations-level (Wang et al., 2018).

Challenges.

Besides of these mentioned benefits, all of the literature recognizes also multiple challenges in taking advantage of the data. Quite a few of the studies are mainly concentrating on these challenges (Ajmera & Jain, 2019; Denicolai & Previtali, 2020; Gordon & Catalini, 2018; Hopp et al., 2018; Huang et al., 2017). The challenges discussed here are of privacy and security (Kohli & Tan, 2016), controversy of cost savings (Denicolai & Previtali, 2020), and gaining the top management support in trying to implement the big data analytics to advantage from data sharing (Ajmera & Jain, 2019).

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implemented to protect individuals private data, but it simultaneously makes data sharing more difficult (Zheng et al., 2019).

One of the most controversial challenges in data sharing and big data is whether there will be cost savings or not. Some scholars argue for the significant potential for cost savings (Nguyen & Poo, 2016; Hopp, 2018; Ratia et al., 2019), some that it is difficult to estimate (Denicolai & Previtali, 2020; Phillips et al., 2017), and some argue for even higher costs (Kohli & Tan, 2016). The cost savings are argued to come from removing unnecessary tests, drugs, and treatments (Hopp, 2018). Other savings could come from better decision-making, which results in fewer errors, as well as identifying the most cost-effective treatments (Ratia et al., 2019). However, the authors are careful with the arguments, and use words such as “potentially” and “in the right situations”. Authors who do not take a stand-in either way, argue, that it is difficult to estimate without first establishing the effectiveness of the technology in improving outcomes (Phillips et al., 2017). Furthermore, they argue that the usage of big data is simultaneously trying to pursue higher quality and costs savings, but the healthcare model changes significantly, which is discussed below, so it is difficult to say what will happen to costs (Denicolai & Previtali, 2020). Authors who argue for more costs say that as quality improves, costs rise (Kohli & Tan, 2016), but do not argue further on why. One point of view for this is that if the healthcare organization has poor data governance - extension of IT governance -, the costs can be high (Wang et al., 2018). With an appropriate governance structure, there is a potential for lower costs.

Another challenge in adopting big data analytics is gaining top management support (Ajmera & Jain, 2019). In a study conducted in India, this was found the be the most significant barrier to the successful adoption of the new era, including new technology which can leverage big data. Deciding on investing in it is not easy since the investment is extensive, and includes also developing and maintaining an appropriate infrastructure, and training the staff. The top management needs to understand the importance of the technologies, as well as the benefits and costs, in order to be willing to make the decision to invest.

Impact on Healthcare Industry

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“capacity to classify individuals and diseases into several fine-grained sub-populations that significantly differ in their response to treatments” (Denicolai & Previtali, 2020, p.2). The personalization means tailor-made healthcare. The new era of medicine is grounded on an individual precisely, also on their lifestyle and environment. In comparison, the traditional healthcare has more “one size fits all” -approach. Predictive means a shift from treating diseases into preventing them, and not only improving the health of an individual but populations (Hiller, 2016). Participatory means more engagement and connectivity of researchers, care providers, public healthcare, and patients. In addition to this, Health 4.0 also points out the need for modern technologies, that are integrated with big data, and uses artificial intelligence (Ajmera & Jain, 2019).

The proactive and patient-centered healthcare introduces the shift away from traditional reactive and hospital-centered healthcare (Pramanik et al., 2017). As the fourth P, participatory, patients are more engaged in their health, not only by sharing data but also being included in the decision-making (Nguyen & Poo, 2016). In this shared decision-making model, first, the healthcare professional and patient discuss the choices together, where patients’ preferences and uncertainties are addressed. Then they review the different options and finally make the decision of treatment path together. In the new model, the patient is in the center and included in every step of the decision-making process. For this, the patients are expected to monitor and track their own health status.

In order to reach this new era of medicine, healthcare organizations need to build new capabilities. First, they need to develop big data analytics capability (Wang et al., 2018). In healthcare, this is defined as “the ability to acquire, store, process, and analyze large amount of health data in various forms and deliver meaningful information to users that allows them to discover business values and insights in a timely fashion” (Wang et al., 2018, p.6). The big data analytics capability consists further of five different capabilities that are all needed in an organization. Table 4 illustrates the different capabilities and what the purpose of those are.

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Big Data Analytics Capabilities

Analytical capability Used to identify patterns of care

Unstructured data analytical capability Used to analyze semi-structured or unstructured

data

Decision support capability Transferring the data into reports to aid

professionals in decision-making

Predictive capability The ability to make estimations on the future based

on data

Traceability

The ability to track the data throughout the organization to make data consistent, visible, and

easily accessible Table 4 – Big Data Analytics Capabilities

Strategies for healthcare organizations.

The healthcare organizations needs new strategies and changes to build these abovementioned capabilities. The literature suggests different strategies to create capabilities and to being successful in big data analytics (Wang et al., 2018). Strategies start with implementing big data governance. As mentioned before, if the organization has poor IT governance, and thus data governance, the costs can get high, but with appropriate governance, the organization can leverage the benefits. Data governance concentrates on taking advantage of enterprise-wide data resources in order to generate business value, and it includes Master Data Management, Data Life-Cycle Management, and Data Security and Privacy Management. Another strategy is developing an information-sharing culture within an organization. Having this culture will lower the resistance to new systems, and without it, the data collection and delivery would be limited, which would further influence the big data capabilities. In other words, there needs to be a cultural shift to a culture based on prevention and the integration of data (Denicolai & Previtali, 2020). The third strategy is training key personnel to use big data analytics (Wang et al., 2018). As the interpretation of the data is crucial, healthcare employees need to be trained well (Wang & Byrd, 2017), although this brings considerable costs for the organization. The last strategy for building capabilities is generating new business ideas from big data analytics that could, for example, increase productivity or build competitive advantage. These ideas could be based on the big data predictive analytics tools to identify new market trends.

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can interact, for example, with a data analyst. Precision Health System -strategy takes it even further and leverages all different types and sources of data. The data can come from physicians, patients and their families, healthcare organizations, health insurance companies, and other independent actors. Most of the literature is concentrating on The Precision Health System -strategy, since it also includes the personalized healthcare.

All the above mentioned also bring possible changes to payment systems. Currently, the payment structures are mostly based on the number of medical interventions performed, but the new medicine could help it to switch to be instead based on healthy outcomes (Hiller, 2016). This change in the payment structure could have either a positive or negative effect on patients. The positive effect would be that it would be used for preventive purposes, and the overall health of the population will increase. The negative effect would be that it is used for identifying which patients to avoid – who are in the risk group to be more ill. On the other hand, if the payment system is not changed, profit-driven hospitals might still pick the patients based on the revenue they are estimated to generate (Hopp, 2018).

With the new medicine comes new technologies, which means a change also in the healthcare market (Pramanik et al., 2017; Hiller, 2016; Denicolai & Previtali, 2020). IT firms have an increasing role in the healthcare sector by providing, for example, sensors, applications, and wearables. Also, social media companies, like Facebook and Twitter, have a growing role (Denicolai & Previtali, 2020). They own tremendous amounts of data on people’s lifestyle habits, environments, and even their health.

Impact on Professional Roles

The impact of data sharing on professional roles in the healthcare industry is also not a clear cut. With the changes mentioned above, there are estimated to be changes in the professionals' current responsibilities (Salman et al., 2017) and need for training of new skills (Wang et al., 2018). As the delivery model of healthcare would change into a more patient-centered model, the relationship with patients and healthcare professionals is expected to change as well (Qudah & Luetsch, 2019).

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concern from the professional side is that how they will be able to separate professional life from the private one if they are always reachable. On the other hand, they are more likely to be able to ask specific questions from the patients and control the way of discussion, since they have a more coherent picture of the patient's health available. The effect of this is that this new way of delivering healthcare changes the way healthcare professionals spend their time delivering care to patients. More time is used to analyzing data, and less time spent with the patient physically.

The involvement and awareness of patients of their own health, as well as improvements in technology, also influences the decision-making of healthcare professionals (Salman et al., 2017; Varshney, 2014a). Big data could be used to help in prioritizing patients, which used to be the work of doctors or nurses (Salman et al., 2017). The automatic prioritizing has been proven to work with patients with chronic heart diseases, which takes the pressure out from triage nurses who usually perform the prioritizing. On the other hand, as discussed above, the shared decision-making model brings patients more in the center of their care. The patients are more empowered with information and can make better decisions as well as have better overall control of their health (Varshney, 2014a). At the same time, healthcare professionals will have all the essential information to make the best possible decisions (Qudah & Luetsch, 2019). However, some estimates are, that since patients have all this information, the role of the doctor changes more into a consultative role and exists to offer decision support to the patients (Zhang et al., 2017). This estimation is in line with the assessment of shift to professional-managed care.

Switching to more preventive healthcare means that healthcare professionals need to find new ways of working (Hiller, 2016). Preventive healthcare includes new, unfamiliar areas when the patients are continuously being monitored, and the professionals need tools for coping with the change. The training of the professionals could help with this. The professionals need to be able to make predictions and decisions based on the data they receive, and the skills of employees can be a severe barrier in adapting to the new technologies and ways of working (Ajmera & Jain, 2019). The training is suggested to include critical thinking, analytical training (including data interpretation), basic statistics, data mining, and business intelligence (Wang et al., 2018). These skills would help them to analyze the data and make well-educated predictions and decisions based on it.

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Discussion

In the discussion section I will answer my research questions of how the sharing of patient-generated data on their lifestyle can influence the healthcare industry and the professionals in it, suggest directions for future research, explain further the theoretical and practical contributions, and go through some of the limitations of the study.

Impact on Different Levels

It seems to be clear that there would be as many benefits as there are challenges in leveraging this data on many levels, especially in organizational and individual levels. This can also be seen from the codebook (Appendix II). There are 47 quotes for the possibilities and 42 quotes for the difficulties this data sharing could bring. However, the same possibilities and difficulties were mostly repeating in the papers. The benefits chosen to be discussed in this study were improved decision-making, leading to improved quality of healthcare, fairness in selecting patients to receive treatment, and benefits in IT infrastructure and operations. The first benefit would help the healthcare professionals, and the improved quality of healthcare would then benefit everyone receiving health care. The fairness in selecting patients would also benefit everyone, and bring equality to the healthcare sector. The benefits in IT infrastructure and operations are benefits on the organizational level. Already here we can see, that the discussion is revolving around individual and organizational levels. The challenges in privacy and security, controversy in cost savings, and gaining the top management support are also mostly in organizational level. The first clear controversy was indeed found in the debate of cost savings. The authors seemed to be careful of arguing for the cost savings, as it was only mentioned as “potential” benefit (Nguyen & Poo, 2016; Hopp, 2018; Ratia et al., 2019). In the discussion I will mostly concentrate in the improved decision-making from the benefits, and controversy in cost savings and gaining the top management support from the challenges, and what their impact would be.

Impact on the healthcare industry.

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and inter-organizational level. My finding was that most of the literature is focusing on individual or organizational levels. I could not find any literature focusing solely on the team or inter-organizational levels. Likewise, Varshney (2014a) concludes that more research is needed to study the changing role of professionals. Varshney (2014a) also mentions that the impact on individual and team level is significant, medium on the organizational level, and very little on the inter-organizational level. However, the study of Varshney (2014a) focuses only on the literature on mHealth, so there might be a difference. mHealth includes the sensors, wearables, and applications, but in this study, I also included, for example, the business intelligence and analytics side. However, the issues and challenges the author discusses on different levels are still mostly the same. Because of these similarities in issues and challenges, I would argue based on my findings that the most significant impacts are on the team and organizational levels. On the organizational level, the model of delivering healthcare changes into more preventive healthcare and organizations need to build new capabilities in order to leverage from this change. On the team level, the role of professionals changes into having a more consultative role. Nevertheless, change on the industry-level is also remarkable, and even radical, which is explained below.

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Putting all of the above together, collecting and sharing this patient-generated data would challenge the healthcare organizations to change their delivery models. The model would be one where the patient is in the center of the care by having a more active role. Patients will provide data and be highly involved in decision-making. Healthcare organizations need to build new capabilities and have new technologies to support this change. IT firms will also play a role in the market, as they provide devices, and in some cases are the owners of the data. To generalize these findings into the industrywide analysis, I will first elaborate on how industries change over time. Industries change over time along four trajectories, namely radical, progressive, creative, or intermediating (McGahan, 2004). These trajectories set limits on what will generate revenue in business. The trajectories are defined on whether the change is threatening the core activities and or the core assets of the industry. The core activities of the industry are the ones generating profits and can be said to be threatened when they become less essential to the customers and suppliers. In the healthcare industry, the core activity is to provide cure and treatment for diseases (Denicolai & Previtali, 2020). The core assets are the resources, knowledge, and brand capital, and these can be said to be threatened when they do not generate value in the same way anymore (McGahan, 2004). The resources in the healthcare industry are the human capital and technologies, and knowledge the knowledge of the healthcare professionals.

The core activity of the healthcare industry would change to more preventive care from curing and treating illnesses, and the current core assets would not generate as much value as before. The knowledge needs to be updated along with the technology in order for this change to happen, as the organizations need to build new capabilities (Wang et al., 2018). The current technologies and knowledge of the healthcare professionals that are concentrating on the curing and treating will not have the same value to the patients when the other option is to prevent these illnesses from the beginning. These changes in the core activities and assets would mean that the change in the healthcare industry is a radical change, which requires that both the core activities and core assets are being threatened (McGahan, 2004). Progressive change is the most common change in the industries. It, however, means that neither core activities or core assets are being threatened, and thus the basic activities and technologies remain the same. An example of progressive change would be the change in the airlines, where the change is brought by optimizing efficiency. In creative change, only the core assets are being threatened, and intermediating change the core activities.

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with different service providers (Huang et al., 2017), and some new entrants will disrupt the traditional global healthcare market. There is a need to create those ecosystems (Denicolai & Previtali, 2020), where traditional players collaborate more closely with different domains. As discussed before, for example, Twitter and Facebook own tremendous amounts of data on people’s lifestyles, habits, and environments, and this data would help in changing to 4P medicine, but it means collaboration with these firms. However, a radical change takes decades to occur, since it is described to be more evolutionary than revolutionary. The whole industry would be transformed, but the organizations in it have time to develop new strategies to cope with the change. An example of an industry that was, and still is, on radical change trajectory would be letter-delivery business (McGahan, 2004). Use of the Internet made it more popular to send emails, but sending of letters has not died out entirely but is decreasing year by year.

Impact on healthcare professionals.

The most interesting finding on the impact of data sharing on professional roles is that it is just stated as a fact that their profession will change, but this statement is not backed up by evidence. It also seems to be fairly controversial of who makes the decisions on patients health and based on what. The most discussed benefit of having the lifestyle-related data from patients is the improved decision-making of healthcare professionals since they have a more complete picture of the patient’s health and the influencing aspects. However, the new era of medicine is strongly emphasizing on the matter that the patient is participating more in the decision-making process, and the healthcare delivery model shifts to “professional-managed care”. What Qudah and Luetsch (2019) found in their review on what the influence of mobile health applications is on patient-healthcare provider relationship, is that patients had an overall positive image of using mHealth applications, and they would be ready for the transition. However, the healthcare providers were not as positive about using these applications, but their concerns were not discussed thoroughly.

What is left unclear is the actual role of healthcare professionals. They are expected to spend increasing amounts of time in analyzing patients data, which is varying in the means of sources and validity. They would also be expected to have the best knowledge on patients health as well as potential risks. The cooperation between the patient and the healthcare professional, and the impact of this cooperation on the improved decision-making is left unclear. The patients are estimated to feel more empowered and activated (Qudah & Luetsch, 2019), but the long-term effect of less face-to-face contact and involving the patient in the decision-making is still vague.

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a different range of details of the patients, which would have to be taken into consideration. Also, the new healthcare ecosystem will affect them. Patients have access to different sources of information through the Internet, and also, the application providers will be directly involved in the care of patients. The application providers can inform patients of, for example, higher heart rate than normally immediately when this happens.

Future Research

The revolution of shifting into 4P medicine is still in very early stages, and there is a need for more research. The research on industry-level is almost non-existent, and the impact of creating the healthcare ecosystems should be investigated. There exist questions on who owns the data, what are the financial arrangements like in the new ecosystem, and how does the collaboration work between different domains, such as IT firms and hospitals. Additionally, the effect of this on the traditional healthcare industry should be studied.

The possible cost savings should be investigated more since the current literature does not seem to agree on whether there will be cost savings or not. The cost savings are argued to arise from improved decision-making which would result in fewer errors and finding the best treatments (Ratia et al., 2019) as well as removing unnecessary tests, drugs, and treatments (Hopp, 2018). On the other hand, the increased costs are argued to come from a higher quality of healthcare (Kohli & Tan, 2016) or poor data governance (Wang et al., 2018). However, as Denicolai & Previtali (2020) state, it is difficult to estimate what will happen to the costs when the healthcare model changes. The other challenge in adopting big data analytics, which makes the change possible, is gaining the top management support. Since the top management needs to decide on making an extensive investment, the costs need to be known. However, the costs cannot be estimated without understanding the new healthcare delivery model in total. It has also been researched on how difficult it is to put value to IT (Brynjolfsson & Hitt, 2000). Undergoing an organizational transformation and implementing new business processes, as well as acquiring more highly skilled staff, are all negative on the balance sheets. These changes may also have a more positive effect in the long term and can take years of adaptation and investment before the benefits are maximized, and the real costs can be realized. The intangible benefits need to be counted as well, such as the improved quality of care, and the speed of delivering care to patients.

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crucial to understand how more active and empowered patients effect this. The healthcare professionals also had concerns about the adoption of new technologies, which might then further influence how they use it. Technology can bring organizational benefits, but only if it is accepted and used (Venkatesh et al., 2003). The professional’s intention to use could be studied further by using the Technology Acceptance Model (TAM) (Mathieson, 1991). TAM takes into account how the user perceives the usefulness and ease of use of the technology, that determines the attitude towards use, which directly influences the intention to use the system. The intentions then determine the actual system use. Using this model could help the scholars to understand the professional’s point of view for the new technologies.

Contributions and Limitations of the Study

Theoretical and practical contributions.

The previous research did not address the possible change the sharing of patient-generated data could be on the industry-level, and it was concentrating on traditional data sources or data exchange between different institutions. This study brings together different studies on, for example, big data and business analytics, which have been studied instead separately before. It combines different levels of analysis in order to generalize the findings into the level of the industry, which has not been done in the past. However, as we can see from above, all of these levels are highly interconnected. This study brings together those different levels and explains how the data is being generated, how it can be leveraged, and what it means on several levels.

This review provides a deeper understanding of how the different levels are connected and what the change would mean on an industry level, as well as on the professional level, and points out future research areas. For the industrywide analysis this study shows how the change is a radical change. The studies before have mentioned that data sharing can change the industry dramatically, but also on the other hand, that the impact on organizational and inter-organizational levels is not significant. This study shows why the healthcare industry is on a radical change trajectory - the core assets and core activities both change.

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some tasks from professionals, such as prioritizing patients. It was concluded that healthcare professionals have their concerns about adopting all this technology and the changing model, and the fear of losing one’s job could be one of the fears.

In order for the healthcare organizations to survive in this change trajectory, they need to start building new capabilities and cooperating with IT firms. However, the change cannot happen without the patient’s willingness to share their data. They will need to decide on sharing, but the security and privacy issues need to be addressed first. The data standards need to be created, but as mentioned before, it is still unclear who is responsible for this. Only after this, the new business model can work. This study might help the managers in healthcare to understand the importance of what the effects of sharing lifestyle-data would be. As McGahan (2004) states, “The only reasonable approach to radical change is to focus on the endgame and its implications for your company’s current strategy.” (p.89). Even if the change might be as slow as in the letter-delivery business, healthcare organizations should already think of new strategies to be able to survive when the change happens.

For the IT firms that are developing the applications and who might already own a large amount of data, the contribution of this study might be different. The industry convergence is increasing over time, and it is easier for organizations to expand the scope of their services (Kim et al., 2015). The traditional healthcare industry might be triggered by the fear of IT firms merging into the area of healthcare. The IT firms are already providing health-related applications, so they do have the knowledge, and they have the technology needed. The technology converge is the dominant driver of industry convergence (Kim et al., 2015). The last strategy for organizations for building capabilities to make the change happen, was to generate new business ideas from big data analytics, could be more directed at the IT firms rather than healthcare organizations.

Limitations.

There are, however, limitations to this study. First of all, using different or additional search queries could have brought up more researches. The time was limited for conducting this literature view. With more time, possibly more search terms could have been added, especially to try to find more literature on professional roles.

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Appendix I

The number of articles found from Web of Science with the search queries, by year:

Year Number of articles

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Appendix II

Codebook

Category Code Explanation # of

codes

Example

Managerial Change management

Examples of how change management is needed

59 “To create a data-driven organization, practitioners have to identify the strategic and business value of big data analytics, rather than merely concentrating on a technological understanding of its implementation” (Wang et al., 2018)

Strategic choice What possible strategic choices can be made to move forward in healthcare industry

8 “PM is claimed to be an emblematic example of disruptive innovation, which breaks the Porter rules and pursues simultaneously a higher quality of healthcare services and significant cost savings.” (Denicolai & Previtali, 2020)

Current state What is the current state of healthcare industry and professionals in it

10 “…Each year worldwide patients need to stay 2.4 million additional days in hospital purely because of mediation-related errors. These errors also cause 32,000 deaths and $9 billion in costs annually.” (Pramanik et al., 2017)

Needed change What needs to change in order to get to the new state of healthcare

13 “However, optimization of administrative and managerial process is required, e.g. more efficient resource allocation and supply chain management or real-time location for assets and human resources.” (Ratia et al., 2019)

Industrial change

Digital

transformation

Healthcare industrial digital transformation quotes

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healthcare

Ideas where data is used to guide decisions

34 “In smart healthcare systems different smart devices, smart phones, and sensors are used for uninterrupted health monitoring, which can play a significant role in improving healthcare services and assuring real-time responses.” (Pramaniks et al., 2017)

mHealth Examples of using mHealth 13 “The prevention involves mobile health monitoring dealing with activities, exercises, health promotion tools and messages, and caloric and dietary monitoring.” (Varshney, 2014a)

Requirements Requirements for the industrial change to happen

20 “Fourth, as we expected, the findings have highlighted the critical role of absorptive capacity in achieving decision-making effectiveness in health care.” (Wang & Byrd, 2017)

Importance Why is it important for this change to happen

13 “High-quality services are essential in healthcare systems because some serious consequences can result from simple erroneous diagnoses or treatments.” (Pramanik et al., 2017)

Possibility The possibilities that sharing the user-generated data could have (used always together with other code, for example “Possibility” + “mHealth”)

47 “new eHealth applications … promise to increase consumer and provider access to relevant health information, enhance the quality of care, reduce health care errors, increase collaboration, and encourage the adoption of healthy behaviors.” (Kreps & Neuhauser, 2010)

Difficulty The difficulties that sharing the user-generated data could have (used always together with other code, for example “Difficulty” + “mHealth”)

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Professionals change

Professional roles How would this affect the professional roles in healthcare industry

29 “The healthcare delivery model will evolve from the current healthcare professional-controlled care to healthcare professional-managed care.” (Varshney, 2014a)

Patient-driven healthcare

Examples and explanations of patient-driven healthcare

28 “When patients identifies preferences for their primary care, they valued a patient-centered approach, most importantly effective communication, partnership and health promotion.” (Qudah & Luetsch, 2019)

Data & Technology

Big data Examples of big data usage 70 “This layer consists of data nodes and adapters, provides a unified system access interface for multisource heterogeneous data from hospitals, Internet, or user-generated content.” (Zhang et al., 2017)

BI&A Examples of Business Intelligence & Analytics

35 “Indeed, big data analytics is a double-edged sword for IT investment, potentially incurring huge financial burden for healthcare organization with poor governance.” (Wang et al. 2018)

Use of data How data can be used or is used

7 “Auspicious in its breadth, the report notes that because “lifestyle, genomic, medical, and financial data” are needed for the production of predictive analytics, consequently “the distinction between personal data and health-care data has begun to blur”.” (Hiller, 2016)

Wearables Examples of using wearables and their possibilities

5 “These could be implemented in multiple forms such as wearable monitoring systems and sensors in shows to classify daily activities…” (Varshney, 2014a)

For study Background for study

Points for background knowledge

11 “Especially for healthcare industries, healthcare transformation through implementing big data analytics is still in the very early stages.” (Wang et al., 2018)

How is Data Sharing Changing the Healthcare Industry and the Professional Roles in it? Literature Review